Site icon R-bloggers

Intelligent Enterprise: You Can Predict that R Will Succeed

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Analyst David Stodder at Intelligent Enterprise also noted the activity around R at the recent Predictive Analytics World conference in San Francisco, and he reviews his impressions in a column today. In fact, he attributes the increasing prominence of predictive analytics to R:

Possibly the most important factor influencing the spread of predictive analytics is the growing popularity of R, the open-source language and development environment for statistical computing and graphics. Intelligent Enterprise recognized the R Project as a 2010 "One to Watch." Vendors, including IBM SPSS, Information Builders and SAS are incorporating R. As a GNU project, R will make it easier for developers to cost-effectively incorporate predictive analytics tools and algorithms into a variety of applications, services and systems.

David also reported on John Chamber’s talk at the Bay Area User Group meeting, and the REvolution-sponsored social beforehand. On REvolution Computing itself, David had the following to say:

One vendor to watch in the R space is REvolution Computing. Providing software and support to statisticians, data analysts and others using R to develop models and analyze results, the company could become the "Red Hat" of the R community. As Red Hat did with Linux, REvolution Computing is quick to incorporate changes from the R project into its toolkit so that users do not have to monitor developments themselves. An important focus has been to enable developers and statisticians to use parallelism and take advantage of multiprocessor systems and networked computers.

He concludes: "What remains to be seen is how soon predictive analytics tools will become pervasive outside the confines of the statistics and data mining community." With respect to R, I think that depends on two key items: being able to easily apply R to truly large data sets, and making it easier to use for people outside the stats and data mining domains. Those are both areas we’re actively working on, and about which you’ll be hearing more quite soon.

Intelligent Enterprise: Expert Analysis: You Can Predict That R Will Succeed

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.